Heating operation control includes obtaining sensor data indicating measured temperatures within a heating vessel during a heating operation; determining sets of thermal stack parameters. Each set of candidate thermal stack parameters is descriptive of a respective configuration of a thermal stack modeled by a first machine learning model to generate one or more estimated tool temperature values. The thermal stack includes the tool and a part coupled to the tool. Heating operation control also includes determining a temperature profile for the heating operation. The temperature profile is determined, via a second machine learning model, based on the plurality of sets of thermal stack parameters and one or more process specifications of the thermal stack.
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2. The method of claim 1, wherein the temperature profile indicates one or more target interior temperature values, one or more interior temperature change rates, one or more dwell times associated with a particular target interior temperature value, or a combination thereof.
3. The method of claim 1, wherein the heating operation facilitates exothermic curing of one or more materials of the part.
4. The method of claim 1, wherein each set of candidate thermal stack parameters indicates values of one or more boundary conditions, a value of a thickness of the part, a value of a thickness of the tool, one or more temperature values associated with the tool, or some combination thereof.
5. The method of claim 4, wherein the values of one or more boundary conditions comprise values of a first heat transfer coefficient at a first surface of the in-process thermal stack and of a second heat transfer coefficient at a second surface of the in-process thermal stack.
6. The method of claim 1, further comprising determining the plurality of sets of candidate thermal stack parameters based on one or more specified start values, one or more specified step values, and one or more specified stop values.
9. The method of claim 8, wherein the selection criterion specifies selection of a candidate temperature profile based on an associated time duration of the second portion of the heating operation, an associated peak temperature of the second portion of the heating operation, or a combination thereof.
10. The method of claim 7, wherein said obtaining the plurality of candidate temperature profiles comprises determining the plurality of candidate temperature profiles based on one or more specified start values, one or more specified step values, and one or more specified stop values.
12. The method of claim 11, wherein the multiple in-process thermal stacks correspond to two or more cross-sections of the part, two or more cross-sections of the tool, two or more parts, two or more tools, or some combination thereof.
13. The method of claim 11, further comprising selecting the second machine learning model from among a plurality of available second machine learning models based on materials of the multiple in-process thermal stacks and the one or more process specifications of the multiple in-process thermal stacks.
14. The method of claim 1, further comprising selecting the first machine learning model, the second machine learning model, or both, from among a plurality of available machine learning models based, at least in part, on one or more materials of the in-process thermal stack.
17. The non-transient, computer-readable medium of claim 15, wherein the temperature profile indicates one or more target interior temperature values, one or more interior temperature change rates, one or more dwell times associated with a particular target interior temperature value, or a combination thereof.
18. The non-transient, computer-readable medium of claim 15, wherein the heating operation facilitates exothermic curing of one or more materials of the part.
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October 20, 2021
November 12, 2024
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